Optimization of Control for a Hybrid Renewable Energy System with Energy Storage Using Deep Reinforcement Learning Methods

This paper presents a forecasting and optimization framework for the control of a hybrid renewable energy system (HRES) integrating solar, wind, and biomass generation with lithium-ion batteries, electrolyzers, and fuel cells. A bidirectional long short-term memory (bi-LSTM) neural network model was applied for renewable generation and load forecasting, while the deep Q-network (DQN) and soft actor–critic (SAC) algorithms were used for real-time supervisory control of energy storage and hydrogen-based components. The HRES was formulated as a Markov decision process (MDP), where the agents optimize battery charging/discharging, electrolyzer activation, and fuel cell operation under dynamically changing operating conditions. Experimental results demonstrated that the SAC agent achieved more stable learning dynamics and superior operational performance compared to the DQN agent, maintaining an HRES energy imbalance below 0.5 MWh while reducing unnecessary component switching and improving overall system stability. The obtained results confirm the potential of deep reinforcement learning for adaptive and low-emission supervisory control of complex hybrid renewable energy systems.